This position paper analyzes the state of the art in lifecycle-oriented information extraction and knowledge management for MICE and identifies key gaps, including the lack of integrated, lifecycle-oriented pipelines for multi-source data collection and preparation, insufficient alignment between extraction outputs, taxonomies, and KPI-oriented knowledge, and limited operationalization through information access and retrieval.
Researchers and developers face significant barriers when conducting systematic analyses across multiple open data portals or building tools that interact with diverse portal infrastructures. While production-grade systems like Piveau and ckanext-harvest serve portal operators for large-scale metadata harvesting, they...
Tobias Siebenlist, Jennifer Gnyp· EGOV-CeDEM-ePart 2026· 0 citations
LLMCrater is a lifecycle-aware metadata generation framework that combines Large Language Models (LLMs) with stage-specific RO-Crate metadata profiles that progressively enriches metadata across four research lifecycle stages while remaining compatible with RO-Crate~1.1 and EOSC metadata recommendations.
Dani Termaat, N. Soveizi, Zhi-Ming Zhao et al.· 0 citations
A practical, quality-assured method for publishing KOS-related RFPs as context-bearing registry records is contributed by integrating a two-track pipeline—scripted page generation for structured fields and generative summarization for narrative RFP text—with HITL governance for expert verification and correction.
The five-layer approach to using this information asset gives practitioners and researchers a reference architecture for designing, evaluating, and governing enterprise knowledge graph deployments built on proprietary organizational data.
The results show that structured RDF pipelines currently provide the most stable integration behavior, whereas JSON and text pipelines remain more sensitive to errors in mapping, extraction, and linking.
This paper presents a comparative review and synthesis of the hyperscale data analytics cloud architectures of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). The review characterizes storage, processing, machine learning, data lake, governance and business intelligence services depending on...
Harish Kasireddy· International Journal of Int...· 0 citations
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